html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/1375#issuecomment-311118338,https://api.github.com/repos/pydata/xarray/issues/1375,311118338,MDEyOklzc3VlQ29tbWVudDMxMTExODMzOA==,806256,2017-06-26T16:55:08Z,2017-06-26T16:55:08Z,NONE,"In case you're still looking for an application, [gene expression from single cells](https://github.com/olgabot/macosko2015) (see `data/00_original/GSM162679$i_P14Retina_$j.digital_expression.txt.gz`) is very sparse due to high gene dropout. The shape is `expression.shape (49300, 24760)` and it's mostly zeros or nans. A plain csv from this data was 2.5 gigs, which gzipped to 300 megs.

[Here](https://github.com/olgabot/macosko2015/blob/master/notebooks/05_combine_retina_data.ipynb) is an example of using `xarray` to combine these files but my kernel keeps dying when I do `ds.to_netcdf()` :( 

Hope this is a good example for sparse arrays!","{""total_count"": 3, ""+1"": 3, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,221858543